lama

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import random
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import colorsys
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import numpy as np
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import matplotlib
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matplotlib.use('agg')
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import matplotlib.pyplot as plt 
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from matplotlib.colors import LinearSegmentedColormap
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def generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False):
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    # https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
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    """
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    Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks
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    :param nlabels: Number of labels (size of colormap)
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    :param type: 'bright' for strong colors, 'soft' for pastel colors
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    :param first_color_black: Option to use first color as black, True or False
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    :param last_color_black: Option to use last color as black, True or False
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    :param verbose: Prints the number of labels and shows the colormap. True or False
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    :return: colormap for matplotlib
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    """
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    if type not in ('bright', 'soft'):
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        print ('Please choose "bright" or "soft" for type')
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        return
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    if verbose:
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        print('Number of labels: ' + str(nlabels))
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    # Generate color map for bright colors, based on hsv
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    if type == 'bright':
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        randHSVcolors = [(np.random.uniform(low=0.0, high=1),
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                          np.random.uniform(low=0.2, high=1),
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                          np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]
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        # Convert HSV list to RGB
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        randRGBcolors = []
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        for HSVcolor in randHSVcolors:
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            randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))
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        if first_color_black:
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            randRGBcolors[0] = [0, 0, 0]
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        if last_color_black:
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            randRGBcolors[-1] = [0, 0, 0]
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        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
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    # Generate soft pastel colors, by limiting the RGB spectrum
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    if type == 'soft':
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        low = 0.6
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        high = 0.95
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        randRGBcolors = [(np.random.uniform(low=low, high=high),
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                          np.random.uniform(low=low, high=high),
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                          np.random.uniform(low=low, high=high)) for i in range(nlabels)]
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        if first_color_black:
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            randRGBcolors[0] = [0, 0, 0]
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        if last_color_black:
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            randRGBcolors[-1] = [0, 0, 0]
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        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
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    # Display colorbar
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    if verbose:
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        from matplotlib import colors, colorbar
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        from matplotlib import pyplot as plt
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        fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))
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        bounds = np.linspace(0, nlabels, nlabels + 1)
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        norm = colors.BoundaryNorm(bounds, nlabels)
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        cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,
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                                   boundaries=bounds, format='%1i', orientation=u'horizontal')
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    return randRGBcolors, random_colormap
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